The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Predicting Network Activity from High Throughput Metabolomics
Open accessAbstract
The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., & Siuzdak, G. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,262
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Pluskal, T., Castillo, S., Villar-Briones, A., & Orešič, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-395 · Cited by 3,845
Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395
From genomics to chemical genomics: new developments in KEGG
Kanehisa, M. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj102 · Cited by 3,120
Metabolomics: the apogee of the omics trilogy
Patti, G. J., Yanes, O., & Siuzdak, G. (2012) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3314 · Cited by 2,571
METLIN—a metabolite mass spectral database
Smith, C. A., Maille, G. O., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., Custodio, D. E., Abagyan, R., & Siuzdak, G. (2005) · Therapeutic Drug Monitoring · doi:10.1097/01.ftd.0000179845.53213.39 · Cited by 2,226
Identifying biological themes within lists of genes with EASE
Hosack, D. A., Dennis, G., Sherman, B. T., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-10-r70 · Cited by 1,950
Cited by
Metabolomics: beyond biomarkers and towards mechanisms
Johnson, C. H., Ivanisevic, J., & Siuzdak, G. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2016.25 · Cited by 2,829
Identification of bioactive metabolites using activity metabolomics
Rinschen, M. M., Ivanisevic, J., Giera, M., & Siuzdak, G. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0108-4 · Cited by 1,008
Antibiotics-Driven Gut Microbiome Perturbation Alters Immunity to Vaccines in Humans
Hagan, T., Cortese, M., Rouphael, N., Boudreau, C., Linde, C., Maddur, M. S., Das, J., Wang, H., Guthmiller, J., Zheng, N.-Y., et al. (2019) · Cell · doi:10.1016/j.cell.2019.08.010 · Cited by 572
Self-citationSun, Y. V., & Hu, Y.-J. (2016) · Advances in Genetics · doi:10.1016/bs.adgen.2015.11.004 · Cited by 459
Recent advances in the application of metabolomics for food safety control and food quality analyses
Li, S., Tian, Y., Jiang, P., Lin, Y., Liu, X., & Yang, H. (2021) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2020.1761287 · Cited by 394
Self-citation
Metadata
- Authors
- Li, S., Park, Y., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., Jones, D. P., & Pulendran, B.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-07-04
- Volume
- 9
- Issue
- 7
- Pages
- e1003123
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2025944550
- Cited by
- 936
- References
- 72
- FWCI
- 6.99
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Predicting Network Activity from High Throughput Metabolomics. https://app.cochat.ai/papers/10.1371/journal.pcbi.1003123
- The paper
- Li, S., Park, Y., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., Jones, D. P., & Pulendran, B. (2013). Predicting Network Activity from High Throughput Metabolomics. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1003123
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
